@Article{journals/nca/ZahiriA14,
author = "A. Zahiri and H. Md. Azamathulla",
title = "Comparison between linear genetic programming and {M5}
tree models to predict flow discharge in compound
channels",
journal = "Neural Computing and Applications",
year = "2014",
number = "2",
volume = "24",
pages = "413--420",
keywords = "genetic algorithms, genetic programming, compound
channels, linear genetic programming, m5 tree decision
model, stage-discharge curve",
bibdate = "2014-01-21",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/nca/nca24.html#ZahiriA14",
URL = "http://dx.doi.org/10.1007/s00521-012-1247-0",
DOI = "doi:10.1007/s00521-012-1247-0",
size = "8 pages",
abstract = "There are many studies on the hydraulic analysis of
steady uniform flows in compound open channels. Based
on these studies, various methods have been developed
with different assumptions. In general, these methods
either have long computations or need numerical
solution of differential equations. Furthermore, their
accuracy for all compound channels with different
geometric and hydraulic conditions may not be
guaranteed. In this paper, to overcome theses
limitations, two new and efficient algorithms known as
linear genetic programming (LGP) and M5 tree decision
model have been used. In these algorithms, only three
parameters (e.g., depth ratio, coherence, and ratio of
computed total flow discharge to bank full discharge)
have been used to simplify its applications by
hydraulic engineers. By compiling 394 stage-discharge
data from laboratories and fields of 30 compound
channels, the derived equations have been applied to
estimate the flow conveyance capacity. Comparison of
measured and computed flow discharges from LGP and M5
revealed that although both proposed algorithms have
considerable accuracy, LGP model with R-squared = 0.98
and RMSE = 0.32 has very good performance.",
}